Each day individuals and companies receive multiple voice or audio messages. These voice messages can include personal greetings and information or business-related instructions and information. In either case, it may be useful or required that the voice messages be transcribed in order to create written records of the messages. For example, vendors may create paper versions of orders placed via voice messages, lawyers may create paper copies of messages received from clients, and federal agencies may create paper copies of voice messages for public records. In each situation, it is generally important that voice messages be transcribed correctly.
Software currently exists that generates written text based on audio data. For example, Nuance Communications, Inc. provides a number of software programs, trademarked “Dragon,” that take audio files in .WAV format, .MP3 format, or other audio formats and translate such files into text files. The Dragon software also provides mechanisms for comparing audio files to text files in order to “learn” and improve future transcriptions. The “learning” mechanism included in the Dragon software, however, is only intended to learn based on a voice dependent model, which means that the same person trains the software program over time. In addition, learning mechanisms in existing transcription software are often non-continuous and include set training parameters that limit the amount of training that is performed.